Hrl Laboratories, LLC

Gm Global Technolgoy Operations, Inc.

Sasha Strelnikoff

Praveen K Pilly

Aruna Jammalamadaka

Alireza Esna Ashari Esfahani

Nicholas A Ketz

Dana M Warmsley

A method for language processing for a vehicle includes receiving an input text. The input text includes a plurality of words. The method also includes determining a rule-based action representation of the input text, parsing the input text to produce a parsed text. The method also includes determining a model-based action representation of the parsed text. The method also includes determining a final action representation of the input text based at least in part on the rule-based action representation and the model-based action representation.

Language processing for a vehicle

Described is a system for semantically grounded video generation. In operation, the system receives a raw video frame of a scene from one or more sensors on an autonomous platform and then encodes the video into a low-dimensional representation of the scene. The low-dimensional representation is then decoded and fed into a controller model for the autonomous platform. Semantic concepts are extracted and then fed into a world model to predict state and action dynamics of the autonomous platform. A raw observation space is then fed into discriminator networks that operate on frames and videos to determine between real and synthetically generated content. The generative capability of one or more encoders and decoders are modified such that the discriminator networks are unable to distinguish between real and synthetically generated content. Finally, the system recursively generates semantically grounded videos using a conjunction of the world model and controller model.

System and method for semantically grounded video generation

Described is a system for producing a causal map from a body of text. The system receives multiple textual documents as input. Pairs of cause-effect phrases are extracted from the textual documents and embedded into a vector space. The embedded data is clustered into clusters using a probabilistic technique. A causal map having nodes and edges is generated from the clusters. Using the causal map, causal connections between clusters are obtained, where each node represents an event and each edge represents a causal relationship between events. The causal map is provided as an interactive graph.

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Seattle, WA, United States of America

Sasha Strelnikoff